This study uses positional analysis to describe the student interaction networks in four research-based introductory physics curricula. Positional analysis is a technique for simplifying the structure of a network into blocks of actors whose connections are more similar to each other than to the rest of the network. This method describes social structure in a way that is comparable between networks of different sizes and densities and can show large-scale patterns such as hierarchy among positions. We detail one positional analysis method and apply it to class sections of Peer Instruction, SCALE-UP, ISLE, and Minnesota Model context-rich problems. At the level of detail shown in the blockmodels, most of the curricula are more alike than different, showing a late-term tendency to form coherent subgroups that communicate actively among themselves but have few interposition links. Initial position assignments tend to change from beginning to end of the term, but in cases where the initial assignment is stable, those students appear to become more connected to each other and to the largest network component. These trends in position structure and stability may be network signatures of active learning classes, but wider data collection is needed to investigate.

中文翻译：

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物理活跃学习环境中的网络位置

本研究使用位置分析来描述四个基于研究的入门物理课程中的学生互动网络。位置分析是一种用于将网络的结构简化为参与者块的技术，这些参与者的连接彼此之间的联系比与网络其余部分的联系更为相似。该方法以一种在大小和密度不同的网络之间可比较的方式描述社会结构，并且可以显示大规模模式，例如职位之间的等级制度。我们详细介绍了一种位置分析方法，并将其应用于Peer Instruction，SCALE-UP，ISLE和Minnesota Model上下文相关问题的类部分。就模块模型中显示的详细程度而言，大多数课程都是相似的，而不是不同的，显示近期趋势形成连贯的子群，这些子群之间相互交流活跃，但几乎没有中介链接。初始职位分配往往从学期开始到结束都发生变化，但是在初始分配稳定的情况下，这些学生似乎彼此之间以及与最大的网络组成部分之间的联系越来越紧密。职位结构和稳定性的这些趋势可能是活跃学习班级的网络签名，但是需要进行更广泛的数据收集才能进行调查。